Advancing Narrow Space Parking with Latent Memory-Based Reinforcement Learning

Parking in narrow vertical parking spaces presents significant challenges for Autonomous Parking Assistance (APA) systems. To address this challenge, a Latent Memory-Based Soft Actor-Critic (LM-SAC) algorithm has been presented which leverages latent memory to implicitly store contextual information during the parking process, enabling the agent to precisely maneuver in confined spaces. Meanwhile, a Progressive Milestone Training (PMT) method was proposed that decomposes the task into a series of intermediate sub-tasks and integrates curriculum learning to enhance training efficiency. Additionally, a virtual parking environment tailored for narrow spaces was developed, featuring real-sized modeling and high-precision collision detection, with vehicle-mounted Virtual-LiDAR Detection (VLD) to represent surrounding obstacle information. Experimental results demonstrate the efficacy of proposed approach, which achieves a parking success rate of 90%, with an average horizontal error of 1.26 cm and a heading error of 0.38 degrees in challenging vertical parking scenarios, where the opposing space is 4.5m wide and the vehicle length is 5.25m.